The list of potential uses for AI in climatetech is growing fast: developing better materials, optimizing solar farms, integrating renewables and microgrids. But many of these are still theoretical. We wanted to find a real-world application that changed the way we make climatetech.

So we decided to come up with our own test run.

Back in March Duncan Campbell, vice president at Scale Microgrids, used ChatGPT to code some battery dispatch software and tweeted about his experience. Duncan isn’t a professional software developer, but he still came up with some promising results.

Could a non-coder like Duncan use AI to do the work of several climatetech coders?

We invited Duncan to do it again and ramped up the challenge. We recruited Seyed Madaeni, CEO and co-founder of Verse to create a challenge for Duncan. Seyed is an expert in AI and the software used in electricity markets. He routinely sends “problem statements” to his team of software developers to create new software. This time, he sent a problem statement to Duncan that reflects real world conditions, one that we might actually assign to real engineers to solve.

The challenge? Develop battery dispatch software using ChatGPT.

In this episode, Duncan presents his results to Shayle and Seyed. They talk about things like:

The different methods of optimizing battery dispatch, from old-school Excel sheets to more sophisticated software written by coders

Seyed’s process of assigning a problem statement to his engineering team and the simplified version he sent to Duncan

Duncan’s process of iteratively working with ChatGPT-4 to develop and debug the code

Why working with ChatGPT is like working with a bunch of really fast, but really inexperienced junior coders

If you want to see the code that Duncan wrote with ChatGPT, click here.